A novel rough value set categorical clustering technique for supplier base management

Uddin, Jamal; Ghazali, Rozaida; Deris, Mustafa Mat; Iqbal, Umer; Shoukat, Ijaz Ali

doi:10.1007/s00607-021-00950-w

Cited by 4 publications

(4 citation statements)

References 52 publications

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“…However, a challenge arises when clustering attributes possess zero or equal significance values, leading to random attribute selection. To address this issue, the MVA algorithm [84] was proposed, which overcomes the limitations of ITDR but requires further analysis in combination with other rough purity approaches (RPA).…”

Section: Discussionmentioning

confidence: 99%

“…MVA [84] the concept of a number of automated clusters (NoACs) with a rough value set MDA [103], MSA [179], ITDR [62] Moreover, Ammar et al integrate possibility theory with RST, aiming to manage uncertainty in attribute values by utilizing possibility degrees and uncertain clusters through possibilistic membership degrees. This approach extends their prior work [180] by employing a discretization method to convert numeric values into semantically more meaningful linguistic variables with possibilistic memberships based on the K-modes algorithm [167].…”

Section: Uddin Et Al (2021)mentioning

confidence: 99%

“…Therefore, Uddin et al introduced the maximum value attribute (MVA) algorithm [84], which integrates the concept of the number of automated clusters (NoACs) to improve cluster purity while reducing complexity compared to other existing rough sets-based clustering algorithms. The MVA algorithm, which adopts the principles of RST, contains three main steps: (1) computing the value sets for each attribute, (2) determining the cardinality of each attribute value set, and (3) selecting the clustering attribute based on the maximum cardinality of the value set.…”

Section: • Rst Based On Information Theorymentioning

confidence: 99%

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Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Cendana,

Kuo

2024

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Numerous real-world applications apply categorical data clustering to find hidden patterns in the data. The K-modes-based algorithm is a popular algorithm for solving common issues in categorical data, from outlier and noise sensitivity to local optima, utilizing metaheuristic methods. Many studies have focused on increasing clustering performance, with new methods now outperforming the traditional K-modes algorithm. It is important to investigate this evolution to help scholars understand how the existing algorithms overcome the common issues of categorical data. Using a research-area-based bibliometric analysis, this study retrieved articles from the Web of Science (WoS) Core Collection published between 2014 and 2023. This study presents a deep analysis of 64 articles to develop a new taxonomy of categorical data clustering algorithms. This study also discusses the potential challenges and opportunities in possible alternative solutions to categorical data clustering.

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Section: Discussionmentioning

confidence: 99%

Section: Uddin Et Al (2021)mentioning

confidence: 99%

Section: • Rst Based On Information Theorymentioning

confidence: 99%

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Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Cendana,

Kuo

2024

MAKE

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“…Hence, MMeMeR technique was termed effective over existing SDR, MMeR and MMR techniques. Recently, Maximum Value Attribute (MVA) technique is suggested that efficiently cluster the uncertain categorical data [84]. A supplier's data and several UCI data sets are considered to validate the performance of MVA technique with existing approaches.…”

Section: Plos Onementioning

confidence: 99%

Rough set based information theoretic approach for clustering uncertain categorical data

et al. 2022

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Motivation Many real applications such as businesses and health generate large categorical datasets with uncertainty. A fundamental task is to efficiently discover hidden and non-trivial patterns from such large uncertain categorical datasets. Since the exact value of an attribute is often unknown in uncertain categorical datasets, conventional clustering analysis algorithms do not provide a suitable means for dealing with categorical data, uncertainty, and stability. Problem statement The ability of decision making in the presence of vagueness and uncertainty in data can be handled using Rough Set Theory. Though, recent categorical clustering techniques based on Rough Set Theory help but they suffer from low accuracy, high computational complexity, and generalizability especially on data sets where they sometimes fail or hardly select their best clustering attribute. Objectives The main objective of this research is to propose a new information theoretic based Rough Purity Approach (RPA). Another objective of this work is to handle the problems of traditional Rough Set Theory based categorical clustering techniques. Hence, the ultimate goal is to cluster uncertain categorical datasets efficiently in terms of the performance, generalizability and computational complexity. Methods The RPA takes into consideration information-theoretic attribute purity of the categorical-valued information systems. Several extensive experiments are conducted to evaluate the efficiency of RPA using a real Supplier Base Management (SBM) and six benchmark UCI datasets. The proposed RPA is also compared with several recent categorical data clustering techniques. Results The experimental results show that RPA outperforms the baseline algorithms. The significant percentage improvement with respect to time (66.70%), iterations (83.13%), purity (10.53%), entropy (14%), and accuracy (12.15%) as well as Rough Accuracy of clusters show that RPA is suitable for practical usage. Conclusion We conclude that as compared to other techniques, the attribute purity of categorical-valued information systems can better cluster the data. Hence, RPA technique can be recommended for large scale clustering in multiple domains and its performance can be enhanced for further research.

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